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Advances and challenges in plastic detection: a critical review of studies employing hyperspectral data
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This review pulls together 29 studies on using satellite, drone, and airborne cameras that detect light beyond what our eyes can see to spot plastic pollution in the environment—an important tool since finding where plastic waste accumulates is the first step to cleaning it up before it breaks down into microplastics that can enter our food and water. The technology works well for common plastics like grocery bags and packaging foam but struggles with clear plastics or ones that are wet or worn down, meaning our current tracking of plastic pollution likely misses a lot of what's actually out there. The researchers say better cameras and more consistent testing methods are needed before this
Plastic pollution is a growing global problem with severe environmental impacts. Hyperspectral remote sensing offers promising capabilities to detect plastics through their distinctive spectral signatures, enabling monitoring across different environments. Despite rapid technological advances and increasing research activity in this field, existing reviews rarely integrate hyperspectral laboratory spectra with image-based hyperspectral data from unmanned aerial vehicles, airborne sensors, and satellite platforms for plastic detection across diverse environments. This review is based on a structured literature search of the Web of Science, identifying studies published between 2014 and 2025; twenty-nine met the inclusion criteria and were examined to summarize the plastics investigated, the platforms and sensors used, data characteristics and analytical approaches. Key advances include the creation of spectral libraries, identification of diagnostic wavelengths in the shortwave infrared region and improvements in sensors and classification methods. Studies using unmanned aerial vehicles remain rare and often lack shortwave infrared coverage. Satellite observations provide wide geographical coverage but are limited in spatial and spectral resolution. Plastics such as polyethylene, polypropylene and polystyrene tend to exhibit more distinct spectral features and are more easily detected compared with polyethylene terephthalate, polyvinyl chloride or transparent plastics, particularly when wet or degraded. Detection performance also declines in the presence of environmental interference, mixed pixels and insufficient atmospheric or geometric correction. Future research should prioritize standardized datasets, integration of laboratory and image-based data using simulated and unmanned aerial vehicle platforms with full visible to shortwave infrared capability, and development of classification models validated under real environmental conditions.
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Advances and challenges in plastic detection: a critical review of studies employing hyperspectral data
AI summary Read the abstract
This review pulls together 29 studies on using satellites, drones, and special cameras that read light beyond what our eyes can see (hyperspectral imaging) to spot plastic pollution in the environment. The technology works better for some plastics (like common packaging materials) than others, and still struggles when plastic is wet, degraded, or mixed with other debris—meaning better tools are needed before we can reliably track where plastic waste ends up in our air, water, and soil. This matters because the more we can map plastic pollution at its source, the better equipped we are to reduce the microplastics that eventually make their way into our food, water, and bodies.
Advances and challenges in plastic detection: a critical review of studies employing hyperspectral data
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This review pulls together nearly a decade of research on using satellites, drones, and special cameras to spot plastic pollution from above by detecting its unique light signature. The technology works better for common plastics like plastic bags and bottle caps, but struggles with clear plastics or those that are wet, weathered, or mixed with other debris—meaning our current tools may be undercounting how much plastic pollution is actually out there, including the waste that eventually breaks down into microplastics in our water and food supply. Better detection methods could eventually help track and clean up plastic pollution before it fully breaks down into the tiny particles now being found in human blood,
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
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This review evaluates the current state of satellite and aerial remote sensing for detecting marine plastic debris, noting that while progress has been made using optical and hyperspectral imaging, significant challenges remain including low detection resolution for small particles, confusion with other floating materials, and the need for better machine learning algorithms. The paper is relevant to the microplastics field as large-scale monitoring tools are needed to track plastic pollution distribution and inform cleanup and policy efforts, though direct detection of microplastics (<5 mm) from orbit remains largely out of reach with current technology.
Hyperspectral remote sensing as an environmental plastic pollution detection approach to determine occurrence of microplastics in diverse environments
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Researchers tested whether hyperspectral remote sensing technology could detect microplastics mixed into different environmental surfaces like soil, water, concrete, and vegetation. Using near-infrared and short-wave infrared imaging, they achieved over 90% accuracy in detecting and classifying six common plastic types at concentrations as low as 0.15%. The study suggests that remote sensing could become a practical, large-scale tool for monitoring microplastic pollution across diverse environments.
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Researchers evaluated the use of hyperspectral satellite and airborne imagery to detect floating plastic debris in rivers and oceans, addressing major challenges related to plastic spectral properties in field conditions. Remote sensing tools for plastic detection are important for large-scale monitoring of the macro-scale plastic that eventually becomes microplastics.
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